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Preview

Summary

This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in variable selection and multiple testing, and new examples and applications. It includes an R package for all the methods and examples that supplement the book.

Table of Contents

Preface to the first edition

Preface

Introduction

Classical likelihood theory

Generlized linear models

Quasi-likelihood

Extended likelihood inferences

Normal linear mixed models

Hierarchical GLMS

HGLMs with structured dispersion

Correlated randoms effects for HGLMs

Smoothing

Double HGLMs

Variable Selection and Sparsity Models

Multivariate and Missing Data Analysis

Multiple testing

References

Data index

Author index

Subject index

Author(s) Bio

Youngjo Leeis Professor at Seoul National University, South Korea.

Reviews

Praise for the first edition:

"… This book provides a comprehensive summary of [the authors' past work]. However, it is much more than that, and even statisticians who do not agree with their approach to inference will find much here of interest. … some instructors might find this to be a useful text for a course on generalized linear models. … there are many ideas that will be useful for students to mull over …"– A. Agresti (University of Florida), Short Book Reviews

"The book is well written and replete with examples and discussions. With over 500 references, the authors have amassed an enormous amount of information in a single source." – James W. Hardin, University of South Carolina, in Journal of the American Statistical Association, June 2009, Vol. 104, No. 486

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